It is a crisp December morning, with frost on every leaf and a warm winter sun amidst blue skies. There may even be a robin or two amongst the trees. Christmas is in the air and there is a hive of activity here to deliver something special—a sofa, fridge, or TV—in time for the holiday for thousands of John Lewis customers. As the convoy of shiny black liveried trucks, each laden with its stock of large items, makes its way out of the site and on towards customers across the country, the scale of this operation becomes apparent.

John Lewis Partnership (JLP) is an iconic British retailer and its renowned customer loyalty has a long heritage. As the retail market and society have changed, particularly post-Covid, the focus on the customer promise has never been higher. For large items like refrigerators, furniture, or anything requiring a two-person delivery, this means delivering what the customer ordered, when they expect it, leaving them delighted with the experience. Every time.

Optimizing and improving the supply chain to support these objectives is a priority for any retailer. Traditionally, this has relied on the deep expertise within supply chain teams to deliver the scale, service quality and efficiencies required by the business. Forward looking retailers are now looking to augment this by starting to explore the promise of a digital twin of their supply chain. The aspiration is to discover novel possibilities and explore opportunities for solving existing and future business challenges.

What is a digital twin?

A digital twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.

Find out more

The John Lewis supply chain team engaged with IBM Client Engineering to explore the initial steps toward a digital twin. Alongside this strategic aim were more tactical, but still important, supply chain planning objectives that were good candidates for a more data-driven approach. Digital twins can serve as a sandbox for testing new supply chain strategies and technologies without affecting the physical supply chain, enabling continuous improvement and learning.

Why IBM Client Engineering?

Using the Value Engineering Method we take a human approach to solving complex business problems with transformative technology. Using a proven co-creation methodology we align experts from both parties to identify a business challenge that could be the focus of a PoX (Proof of Experience).

What data challenges and opportunities do you have?

According to Anthony Olver, Supply Chain Planning Manager for the John Lewis Partnership, “We have the benefit at JLP of many years of historic data but in many different formats. As a result, datasets were in different locations, with differing levels of access and under the purview of different teams within the partnership. It was therefore not always efficient to bring it together in a useful way.”

“We wanted greater visibility of the present and the ability to easily look back to gauge performance and KPIs. We also had a desire to do more modelling of the art of the possible and future scenarios to give the business the right insights to make the right future investments.”

Presenting all this in a user-friendly interface that makes it easy to navigate the past, present, and future in a natural way was important for adoption. Achieving this would allow business users to make the most of the data in a way that is relevant to their role.

Our Partnership Data Plan, which centers around Snowflake, is our strategic approach to address this. Our project highlighted the potential of more real-time data and the benefits of having data in a single logical location. It has given a glimpse of the future.

What was JL’s experience of the Client Engineering process?

“Working with the Client Engineering team provided us with an exciting opportunity to co-create and explore innovative ways to increase efficiency within our supply chain,” said Stewart Dean, Supply Chain Planning & Development Lead, John Lewis Partnership.

Two streams ran at speed in parallel—stakeholder interviews leading to a UI wire-frame design, and development of the UI in React alongside design and population of the data model. These streams were synced through daily stand-ups which provided an opportunity to rapidly pivot and take on new requests, such as including carbon usage and customer satisfaction data.

The momentum this process generated energized the IBM and John Lewis teams. Weekly playbacks were a celebration of the progress and an opportunity for senior stakeholders to provide their input.

What possibilities did this data visualization project highlight?

“We saw the art of the possible and the scale of the opportunity,” says Anthony Olver. “The magic was how IBM created the data model and brought it all together in a UI design such that the data and insights were easily navigable. This has fantastic potential and puts us on the path to a Digital Twin of our supply chain.”

Stewart Dean went on to say, “The IBM team were able to absorb our complex data sets and transform them into a dynamic and interactive MVP performance dashboard, providing our teams with new capabilities, allowing them to easily assimilate data and gain key performance insights in a highly visual way.”

The screenshots below show the complexity within datasets such as for supply chain, customer orders and revenue being represented in relation to each other in a dynamic interactive dashboard.

UI design for supply chain scenario modelling (using dummy data)
UI design for supply chain network performance heatmap (using dummy data)

UI design for supply chain network performance mapped against delivery routes (using dummy data)

Where do you see this going next?

According to Anthony Olver, “This has massive potential in terms of giving us the ability to visualize the supply chain with different perspectives across sites, group, regions, overlaid with specific metrics and timelines. We would have a view of overall performance right down to individual items. This would give us the ability to make data-enriched strategic and tactical decisions.”

Client Engineering is IBM’s investment in its customers to help solve tough business challenges through co-creation. We are designers, data scientists, technology engineers and business analysts who love working with customers and bringing IBM technology to life.

Do you have a business challenge you would like to approach in a different way? Contact your local IBM representative or click the button to learn more.

Learn how client engineering can help you
Was this article helpful?

More from Business transformation

Attention new clients: exciting financial incentives for VMware Cloud Foundation on IBM Cloud

4 min read - New client specials: Get up to 50% off when you commit to a 1- or 3-year term contract on new VCF-as-a-Service offerings, plus an additional value of up to USD 200K in credits through 30 June 2025 when you migrate your VMware workloads to IBM Cloud®.1 Low starting prices: On-demand VCF-as-a-Service deployments begin under USD 200 per month.2 The IBM Cloud benefit: See the potential for a 201%3 return on investment (ROI) over 3 years with reduced downtime, cost and…

IBM Partner Plus Day: Celebrating our collaborative journey with the IBM Ecosystem

4 min read - Today we kicked off IBM Think 2024 in Boston with Partner Plus Day, a full day dedicated to our partners who are driving client innovation, business growth and scaling the adoption of transformative technology such as generative AI. It is also the perfect opportunity to reflect on the progress and success of the IBM Ecosystem, more than a year after we introduced Partner Plus, the program that embodies our new era of partner-first collaboration. Our partners have generated remarkable business…

How will quantum impact the biotech industry?

5 min read - The physics of atoms and the technology behind treating disease might sound like disparate fields. However, in the past few decades, advances in artificial intelligence, sensing, simulation and more have driven enormous impacts within the biotech industry. Quantum computing provides an opportunity to extend these advancements with computational speedups and/or accuracy in each of those areas. Now is the time for enterprises, commercial organizations and research institutions to begin exploring how to use quantum to solve problems in their respective domains. As a Partner in IBM’s Quantum practice, I've had the pleasure of working…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters